Towards the sustainable development of logistics system model: A system dynamics approach

The contradiction between the limited service capacity of system and the explosive growth of demand has hampered the sustainable development of logistics system. Taking into account the structure of logistics system, this study introduces a system dynamics approach to explore the complex correlation and coupling structure of system, analyzes the multiple feedback loops and design the different scenarios. Results show that the validity and rationality of logistics system model, and the error percentage of GDP and logistics demand factors less than 6%. The influence of the investment in reverse logistics, logistics management, information and organizational management factor on the service quality of logistics system increases in turn. Additionally, adjustment of industrial structure has a significant impact on the investment in information management factor, and highway transportation plays a key role in influencing logistics energy consumption and carbon emissions indexes. The findings can provide valuable references and methodologies, as well as support for decision-making in the sustainable development of logistics system.


Introduction
In the new era, the increasing diversity and high-quality logistics demand adds more complexity to the existing logistics system as the consumers' demand and purchase behavior are changing. The solution to this challenge is to improve the coordination among endogenous, exogenous, and symbiosis dynamics factors within the logistics system, and to achieve sustainability and economic growth [1]. However, the increasingly severe threat of internal and external conditions have affected the sustainability of logistics system, the local economy and the environment. Although some mandatory actions are believed to be effective in alleviating problems, an unprecedented incident has severely limited the potential service of logistics system. Furthermore, the negative externalities of logistics transportation freights of heavy-duty vehicles have deemed as one of major source of carbon emissions [2]. It is against the

Structure analysis of logistics system
Logistics system is defined on a micro-scale as an organized system that performs specific functions within a given area [9], which usually incorporates the sophisticated interactions and various feedback between the social, logistics, and economic factors. Although early studies have proved the effect of the organization and coordination, carbon emissions, logistics information and transportation, policy system on logistics processing [4,10], but they are weak in revealing the complexity of the structure analysis of logistics system. Based on the relevant reviews and system elements (e.g., logistics functional and supporting elements, logistics subjects, etc.), logistics system can be divided into four main parts: logistics transportation and distribution, logistics information management, organization command and coordination, and reverse logistics management. As a central component of logistics system, it is possible to employ logistics transportation and distribution for improving system performance, and enhancing the rationality of terminal logistics activities [11]. Logistics information management plays an essential role in monitoring real-time logistics dynamics and ensuring that the system is running smoothly. There are many applications for the warehouse information management, such as the enterprise warehouse planning system, the warehouse management system, and the warehouse control system [11]. Similarly, studies have focused on technological innovations. Cruz-Jesus et al. [12] developed a model using the framework of technology-organization-environment. Relative advantages and compatibility of technology were also analyzed by Yoon [13].
Organization command and coordination is vital in the logistics system, which not only shoulders a crucial role in integrating logistics resource, but also has an inseparable relationship with logistics information management [14]. A new method, which influences the environmental uncertainty in logistics outsourcing relationship, was proposed by Yang and Zhao [15]. With an increasing threat to the system, it is crucial for logistics organizations to shoulder the social responsibility. Sennaroglu and Celebi [16] observed that a significant organization can constantly adjust its behavior to cope with logistics problems.
Recovering valuable materials that exist in the circulation of logistics system is gradually recognized as a critical issue, along with globally emerging environmental awareness and mandatory acts [17,18]. The definition of reverse logistics was firstly proposed by Stock [19]. Later, Rogers and Tibben-Lembke [20] offered its developed definition, which involves the process of planning, implementing, and controlling the effective flow of materials. From 2001 to 2014, its research mainly covered general studies, waste management, recycling, and disassembling [21]. Sung et al. [22] discussed the problem of how to efficiently utilize the sensor data and IoT technology in reverse logistics. Furthermore, the motivations of sustainable reverse logistics focusing on environmental protection have been revealed [23,24]. Ho et al. [24] pointed out that reverse logistics can help achieve economic and environmental goals.

System dynamics methodology
As a method for exploring the complex connections between subsystems and their intricate effects, a system dynamics (SD) method is acknowledged as a powerful approach to deal with linear and non-linear interactions [25]. Which was initially developed by the Massachusetts Institute of Technology (MIT) Sloan School of Management and the MIT System Dynamics Group in the 1960s. In comparison to other regression analysis methods, the SD method has the advantage of studying that is inherent to systems within a long-term dynamic process [26]. At the same time, scholars have expanded the SD modeling towards micro-analytic models for various logistics issues. Hennies et al. [27] applied a mesoscopic SD approach that combined discrete events and continuous flows in a hybrid DES-SD model.
A summary of applying system dynamics is presented in Table 1, it can be noted that there are many possibilities of research to apply the SD approach to various fields, such as the transportation [7], business [28] and the administration sector [29]. Cao et al. [30] developed the SD model to simulate the scenarios on CO 2 emission mitigation in the whole life cycle of the green electric-coal supply chain. Research has also been conducted with regard to SD approach for urban development. Dong et al. [31] employed a SD method to examine the relationship between the implementation strategy of underground logistics system (ULS) and the sustainability of urban transportation and logistics.
Based on the above literature, the contribution of this article can be identified: First, unlike most of the studies that focus on a single process, this study regards logistics system as a whole. A series of key factors for system operations and the external impacts are incorporated, such as the logistics transportation and distribution, organization command and coordination, logistics information management, and especially reverse logistics management. Second, although the SD method has been widely used, only a small percentage of them considers the carbon emissions and reverse logistics management factors. This study investigates the relationship between the economy-operations subsystem, carbon emission subsystem, and logistics service subsystem with the SD model. Third, while several reviews have explored the issues about logistics system, no specific studies have been conducted on the amplitudes of logistics variation rates with the statistical data obtained from different scenarios. We investigate several scenarios to detect the effect of adjusting the system input, transportation mode, and the industrial structure. The findings contribute to simulating the logistics scenarios and providing valuable insights.

Model design and analysis
Modeling on the integrated scheduling of logistics system that recognizes the interdependence for eco-friendly logistics system is necessary. But it is still unclear how a logistics system model can be constructed to evaluate system performance from the systematic perspective. On the basis of the characteristics and structure of the logistics system, a system dynamics approach is applied, which explicitly aims to facilitate the understanding of such complex systems and the construction of models that describe their characteristics [6]. This section follows the three stages of consisting SD model, which are identifying the system boundaries, designing the causal loop diagram, and establishing the stock-flow diagram [32].

System boundaries defining
Logistics system, as an important part of the social-economy, has an inextricably interdependent connection with regional economy. The growth of economic benefit is contributed to improve the service capacity of logistics system, through the investment in logistics technology and equipment. Wang et al. [33] illustrated an IoT-based intelligent logistics dispatching system. A macroscopic system which consists three states such as transportation, activity, and

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Towards the sustainable development of logistics system model: A system dynamics approach environment systems was discussed by Maheshwari [34]. Besides, logistics transportation could cause a rapid increase in logistics energy consumption, resulting in carbon emissions and pollution loss, and increasing the difficulty of the sustainability of logistics system and economy-operations [25,30,35]. Wang et al. [25] proposed a complex system model to simulate the impact of jobs-housing relationship adjustment policies on CO 2 emissions from urban transport. It is therefore necessary to explore the systemic interactions between regional economy, carbon emissions, and logistics system, which is rarely explored by existing studies.
To understand the mutual interactions and influences among main factors, this study considers that the SD modeling is adequately reflected in the chosen boundaries. As shown in Fig 1, it can be divided into three parts: the economy-operations subsystem, the logistics carbon emission subsystem, and the logistics service subsystem. Each subsystem is indicated by the dotted line with different colors, and the mutual effects and interactions of indicators are presented by the arrows.
In the economy-operations subsystem, GDP mediates the investment of primary, secondary, and tertiary industry, and then determines the value of investment in logistics system. The economy field's improvement in freight traffic that links to the supplies shipped and logistics demand parameters will increase the logistics energy consumption, resulting in the discharge fee of carbon emissions and pollution loss which can affect the value of GDP factor. There is a nonlinear relationship between logistics demand and GDP, and the system service capacity has a positive impact on economic growth. Meanwhile, it studies the reaction force of changes in the economy-operations subsystem to other subsystems under the established premise of economics, logistics, and carbon emission.
In the logistics service subsystem, the level of system investment is incorporated into a dynamic system model, which influences the ability of reverse logistics, logistics distribution,

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Towards the sustainable development of logistics system model: A system dynamics approach information supervision, and coordinated organization. The service capacity of logistics system, which is a factor of positive feedback on GDP, has an impact on the logistics demand and freight traffic. Following the inevitable increase in logistics supply capacity, the improvement of logistics system will create the condition for further economic growth.
Carbon policy support and restraint mechanism of the logistics carbon emission subsystem performs a balanced function in the overall logistics system, and this subsystem is regulated by constraint index and support index changes. Logistics transportation sector is considered highly responsible for its deleterious impact on air quality, as it has a side effect on the logistics energy consumption that influences the value of carbon emissions. It is noted that this factor has directly affected the discharge fee of carbon emissions and pollution loss factors, which have gradually put a strain on GDP. Table 2 depicts some selected factors that are employed to create the SD model. It is worth noting that other factors and abbreviations of this model are illustrated in Table A (seen S1 Appendix for more details). Table 2. Main factors and abbreviations of SD model.

Categories
Relative

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Towards the sustainable development of logistics system model: A system dynamics approach

Causal loop diagram
There are complex inter-relationships between internal factors that influence and/or restrict each other [25], it is essential to reveal the mechanism of logistics system through the economic, societal, and environmental aspects. After the boundary clarification process, this section develops the causal loop diagram (CLD), which is a useful tool for demonstrating the feedback structure of system elements, and indicating which factors in the dynamic system cause a change in the other. Based on the identification of causal connections among factors, we employ the SD method to illuminate the trade-off between system indicators and bidirectional causes. The CLD of system model is illustrated in Fig 2. The main factors of feedback loops are connected by arrows. Each arrow, which represents a causal relationship between two factors, is marked with a polarity to indicate the positive or negative influence. The positive polarity means that two factors will change in the same direction, and the negative polarity means the reverse [31]. Moreover, due to the limited space, the fourteen feedback loops in the CLD are shown in Table 3, which includes six reinforcing loops and eight balancing loops.
Reinforcing loop 1 shows that GDP plays an important role in increasing the investment in logistics system (LSI), which is conducive to improving the value of logistics management. Some measures are taken to enhance the performance of logistics distribution, such as increasing the logistics facility input and improving the density of road network, which can bring positive feedback to the service capacity of logistics system (LSSC). Ultimately, the value of GDP will be improved.
Reinforcing loop 7 indicates that GDP has an influence on the investment in reverse logistics (RLIN) through increasing the system investment. Moreover, RLIN effects both the equipment investment of reverse logistics (RLEI) and the ability of reverse logistics (RLA), to a certain extent, which will increase the coordinated development of LSSC and GDP.
Reinforcing loop 8 reveals that GDP reflects the situation of logistics demand (LD) by influencing the value of per capita demand (PCD). The behavior is represented by the LD and LSSC factors with a negative sign. It is attributed to the fact that a surging logistics demand may be unfulfilled. In general, a decrease in the amount of LD leads to a further negative change in the LSSC and GDP. It is noted that satisfying the actual logistics demand is helpful to enhance the system service capability.
Balancing loop 1 demonstrates that there will be a growth of RLIN when the LSI increase by GDP. The higher RLIN, the more it can give rise to the growth of research expenditures. While it will simulate the supplies recycled (SR) parameter to increase the inventory of recyclable materials, and the volume of freight traffic (FT). However, the FT increase will finally affect

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Towards the sustainable development of logistics system model: A system dynamics approach the value of logistics energy consumption (LEC) and carbon emissions (CE), and have the negative impact on GDP.
Balancing loop 4 is a representation of the positive feedback between GDP and the investment in logistics management (LMIN), which is a more effective approach to enhance the training for logistics talent. The higher LDC, the better it can improve the value of FT that results in the increase of LEC and CE in the overall system process. In addition, the increasing pollution loss (PL) factor seriously affects the efficiency of GDP.
Balancing loop 5 expresses that LSI is affected by GDP, and contributes to improving the performance of information management, with a positive correlation among the RE and TIC factors. Enhancement in the ability of reverse logistics is an effective strategy in influencing both the inventory of recyclable materials and the supplies recycled factors. However, the improvement of FT will inevitably lead to the increase of LEC and CE, and cause the output of PL to have a counteractive impact on GDP.

Stocks and flows
To simulate this conceptualized model, the next stage is to move from the qualitative study of the causal loop diagram to a quantitative model, which is the most crucial step in system dynamics. As illustrated in Fig 3, these factors in the stock and flow diagram are divided into the 12 level variables, 18 flow variables, and 95 auxiliary variables. Similarly, through the availability of data and structural equilibrium among indicators, the main factors and formulas of stock-flow mapping can be shown in Table 4.

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Towards the sustainable development of logistics system model: A system dynamics approach (1) Economy-operations subsystem, which specifies the traffic volume of freight and the per-capita disposable income of individuals, is the most important factor in increasing the total logistics demand. GDP is calculated by Eq (1), in which INTEG stands for the function of integral [31]. Eq (2) is helpful to analyze the logistics demand (LD), in which LDGR represents the growth rate of LD, and LDBR illustrates the baffle rate of LD. The freight traffic [36,37] stock is the function of logistics increase, as shown in Eq (3).

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Towards the sustainable development of logistics system model: A system dynamics approach (2) Logistics carbon emission subsystem depicts several factors that have a direct or indirect effect on GDP. In general, if the tolerated threshold of the freight volume and emissions is exceed, it will cause the socioeconomic losses. As illustrated in Eq (4), the carbon emissions factor [38,39] is the function of the increase of carbon emissions (CEI) and the decrease of carbon emissions (CER) value. By varying the standard of CE, PLF, and CEII factors, the results of the discharge fee of carbon emissions (CEDF) and pollution loss (PL) parameters change accordingly. It considers the CEDF and PL as factors to analyze that the impact of the CE on GDP, as shown in Eqs (5) and (6).
(3) As previously stated, the service capacity of logistics system (LSSC) and the investment in logistics system (LSI) are the main factors within the logistics service subsystem. Based on the related literature and expert guidance, LSI relies heavily on the investment of primary, secondary, and tertiary industry. As presented in Fig 3, the coordinated ability (CA) depends on the organization system, technology innovation, organizational guarantee, decision-making, and organizational communication ability. In Eq (9), the technological innovation, network density, and talent benefit factors are selected to present the distribution capability of logistics. Eq (10) illustrates a calculation method of the ability of information supervision (ISA). As seen in Eq (11), the ability of reverse logistics (RLA) is affected by the innovation capability and equipment investment of reverse logistics. The function of the LSSI and LSSD factor is stated in Eq (12).

Model validation and simulation
As the capital of China, Beijing is selected as a study example for the following reasons: First, with accelerated urbanization, changes in the urban functional layout have occurred in recent years, leading to separation of jobs-housing and spatial dislocation phenomena in Beijing [25]. Second, Beijing's logistics system presents some features of the diversified logistics subjects, the "three rings, five belts and multi-centers" of logistics spatial pattern, and the dominant position of terminal logistics. In addition, with the consideration of the pandemic, it's crucial to simulate the current logistics system of Beijing. Since a SD model requires real data to verify its effectiveness, the modeling time horizon of logistics system for the evolution in Beijing is set to 13 years from 2010 to 2022, this paper sets the step size as one year and the model period as 2010. Table 5

Model validation
In purpose of validating the logistics system model, this section has not only presented the running test of model, but also displayed the stability, sensitivity and the historicity test of model.

Running test of model
Its known that the running test includes the examination of model structure and variables units. To check the rationality of the constructed model, we applied the simulated soft to test its structure. "Model is OK" can be illustrated, which presents the structural consistency of model. Moreover, the units check tool is provided to analyze the magnitudes of factors. After several compilation error and follow-up checks, the model is finally made to verify the consistency of dimension, and the correctness of equation.

The stability of model
The next step is to examine the stability of logistics system model. If the value of factors vary significantly under the different time intervals, it means that system is not scientifically stable. Through the method of integral error test, the service capacity of logistics system is investigated in Fig 4. It illustrates that the curve of LSSC is basically consistent from 2010 to 2022, and the model has a good stability. Similarly, other indicators within model also passed this test.

The sensitivity of model
The sensitivity analysis focuses on checking the model's response to changes in input parameters. Taking the CE factor as the example, the simulated result of CE indicator through adjusting the rate of CEI is demonstrated in Fig 5. A change in one parameter does not have a significant effect on the result of model, and the sensitivity of model is good.

Historicity test of model
Taking into account the mandatory frequency of a larger number of factors, the GDP and logistics demand factors in model are selected. This study regards GDP as the level of economic development, and expresses logistics demand as the cargo turnover. Table 6 shows the

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Towards the sustainable development of logistics system model: A system dynamics approach

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Towards the sustainable development of logistics system model: A system dynamics approach error percentage of the simulated and actual value of testing factors. Results show that the error range of GDP from 2010 to 2019 is -6%-0.2%, with an average error of -2.0380%. The error range of logistics demand factor is between -0.02% and 0.07%, an average error of 0.00482%, and the error percentage less than 6%. The high similarity between the simulation values of the real values and model implies that the behavior described by SD model is well consistent with the actual state, and then proves the confidence and validity of modelling.

Results and discussion
Considering the advantages of SD approach, such as the inclusion of external logistics factors, and the limited data, this study first tests the tendency of economic and logistics development in Beijing, and then simulates the behavior mode of system under different scenarios. Some high-leverage solutions are proposed to improve the service level of system, with more attention to the coordinated area growth. Since customer needs are continuously changing, along with the expansion of e-commerce, the logistics industry needs to combine a variety of channels to support the seamless shopping experiences [41,42]. However, practical factors such as insufficient investment in logistics equipment and technologies may hinder the growth of FT factor. From 2015 to 2018, the FT factor in Beijing satisfied the actual demand. It reveals that the logistics industry has gradually paid attention to logistics innovation. Its also related to the fact that LD itself has declined. From 2019 to 2022, capital logistics supply and demand will fork again, and that gap will be narrowed. It is expected that the new intersection point will appear around 2024, which is inseparable from the painful experience of pneumonia forcing logistics to use green technologies or products.

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Towards the sustainable development of logistics system model: A system dynamics approach

Scenarios design and analysis
To accurately comprehend the model and policy adjustment of Beijing, several scenarios of different types are designed, which are the industrial structure adjustment, the transportation mode, and the system input, respectively. At the same time, some incentive strategies are highlighted based on the effect of those scenarios on logistics performance.

(1) Adjustment of industrial structure
To understand the impact of adjusting industrial structure on system service, three scenarios are established, namely, scenario (1) is the original condition; scenario (2) demonstrates that the input coefficient of tertiary industry is increased by 8%, while the input coefficient of primary industry and secondary industry is decreased by 4%; scenario (3) represents that the input coefficient of tertiary industry is decreased by 8%, while the input coefficient of primary industry and secondary industry is increased by 4% respectively. The simulation results are illustrated in Fig 7.  Fig 7(a) shows that the growth rate of LMIN in scenarios is relatively slow from 2010 to 2013, but since 2014, the rate rises again.

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IMIN occupies a leading role with a relative growth of 16.98% by 2022 in scenario (2), while OMIN is about 15.55% times less than the original value by 2022 in scenario (3). Government can recognize the inherent characteristics of logistics information management, and implement the feedback mechanism of coordinated development of Beijing's economy and logistics system. Meanwhile, the industrial structure modes can be maintained, and the link between the logistics industry and other local industries should be strengthened.
(2) Adjustment of system input Since logistics system is influenced by the proportion of LMIN, RLIN, OMIN, IMIN, it's crucial to gauge LSSC with two different strategies. Strategies (i): a certain index of factors such as the LMIN, RLIN, OMIN and IMIN will be modified by 1.5%, while the other indexes will be decreased by 0.5%. Strategies (ii): a certain index of the above parameters will be changed by 1.5%, while the other indexes will not be shown. We find that the results of scenario 3-1 are the best, whereas those of scenario 1-2 are the worst. Increasing the investment of IMIN or OMIN may not be sufficient to improve the value of LSSC. However, the investment of LMIN or RLIN would have a significant influence on the LSSC. Therefore, it is impossible to obtain the best logistics service by emphasizing only logistics management without involving reverse logistics management. The cooperation between

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Towards the sustainable development of logistics system model: A system dynamics approach recovery sectors is emphasized, since it strengthens the green logistics practice. We will pay particular attention to seeking the breakthrough point of shared technology and logistics system in Beijing.
To reveal the different influence of LMIN, IMIN, RLIN and OMIN on LSSC, a series of results for LSSC have been designed in Fig 9. Fig 9(a) depicts that if the input rate of LMIN is increased by 1.5%, while the others remain unchanged, LSSC will increase to 19852100 (Dmnl) in 2022. Otherwise, it will drop to 14826700 (Dmnl) by 2022, an increase of 11.88% over year. Fig 9(b) shows that IMIN helps the LSSC reach 18031200 (Dmnl) by 2022, which may increase by 7.36% compared with the strategy of decreasing the input rate. Fig 9(c) describes that the RLIN will rise to 22733600 (Dmnl) in 2022, under the scenario that only the growth rate of RLIN is changed. Results of the OMIN changes by 1.5% are depicted in Fig 9  (d), which indicates a slight change of OMIN among the three scenarios.
On the whole, the result of LSSC 3-1 is the best, while that of the LSSC 1-2 is the worst. It indicates that increasing the input rate of RLIN alone or decreasing LMIN by the same multiple can have a significant impact on the LSSC. Some insightful suggestions are given for Beijing to build the logistics network, which is the "logistics base+logistics (distribution) center+terminal distribution". Technologies are used to adjust organization patterns and the service patterns of green logistics, which are the desirable options for balancing the natural environment, energy demand and economic development. Although it is possibly related to coefficient setting of a few factors, the results may expose the internal mechanism to a large extent. Efforts should be made to enhance logistics service capability in Beijing such as cultivating the guidance for reverse logistics, promoting the deep integration of technology and system as well as developing new forms of green logistics. It is of utmost importance to increase the input of reverse logistics management, and establish the operation mechanism of reverse logistics.

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Towards the sustainable development of logistics system model: A system dynamics approach

(3) Adjustment of transportation mode
As the reduction of carbon emissions has been an urgent issue in developing a low-carbon economy, the impact of logistics energy consumption (LEC) on carbon emissions (CE) is substantially analyzed. The following policy scenarios can be created: scenario (1) displays the initial settings; scenario (2) shows that the percentage of highway transportation ascends by 6%, while the percentages of railway, civil aviation, and pipeline transportation decrease by 2%; scenario (3) and scenario (2) have opposite settings. Results are demonstrated in Fig 10. Although the value of LEC has been increasing, its growth rate has slowed down from 2010 to 2014, as illustrated in Fig 10(a). The carve of LEC 2 will rise up to 18.287% in 2022 in comparison to LEC 3. Based on the above discussion, it is clear that the change of LEC will certainly affect carbon emissions. Fig 10(b) displays that the gap of CE carve remains unchanged in 2010 and 2011 years, and then this gap expands slowly. In 2022, results of CE2 may increase by 1.68% over the baseline scenario.
In comparison to the mode of civil aviation, pipeline and railway, highway transportation plays a key role in influencing the LEC and CE indexes. Statistical data presents that highway transportation within the dynamic system has a high sensitivity. To promote the long-term growth of logistics system, promulgating the policies of carbon emission control, and the carbon pricing and emissions trading are imperative [43]. It enables logistics managers to decide the optimal logistics recycling modes in accordance with carbon tax policy. Moreover, government should promote the publicity of low carbon awareness, and strengthen the implementation of green energy technologies. Companies can make more investment in low-carbon technology R&D, and increase the application of low-carbon technology, and pay more attention to emission-reduction technologies by building a carbon asset system.

Conclusions and future research
According to the characteristics of the sustainability of logistics system such as dynamics and complexity, there is a lack of systematic evolution to reveal the interaction, feedback of factors within the system. Moreover, the obscured definition of system structure has made it difficult to evaluate the extensive development of the economy, environment, and logistics system of a given area. A set of boundaries involving the economy-operations subsystem, logistics carbon emission subsystem, and the logistics service subsystem are organized into a causal loop diagram, which is then converted to the stock and flow diagram of logistics system model. The following conclusions are drawn in detail below: Firstly, the service capacity of logistics system (LSSC) and GDP factors have positive feedback loop in the process of a whole logistics system. Although there is a positive interaction between GDP and the freight traffic factors, surging logistics energy consumption is insufficient for the further progress of economy. Secondly, based on the validity and reality of logistics system model, results not only verify adjustment of the industrial structure has a direct impact on the system, but also reveal the different change in LMIN, IMIN, OMIN and RLIN factors. More specially, the IMIN indicator occupies a leading role can be explored. Furthermore, adjustment of system input shows that the LSSC is influenced by the investment in reverse logistics, logistics management, information and organizational management factor, and its degree of influence increases in turn. Finally, in comparison to the mode of civil aviation, pipeline and railway, highway transportation plays a key role in influencing the LEC and CE indexes. Adjusting transport mode depicts that model has a high sensitivity to the highway transportation.
Further work focuses on extending this model to the international cities to help them evaluate the impact of multiple policies on system. In order to properly meet the extensive needs of internal and external system elements, it is necessary to modify or add more parameters according to the various circumstances. Second, content analysis and expert consultations can assist in addressing the above limitation in the future. Third, the integrated model can be used to examine various scenarios, and address some issues about the long-term behavior of the intricate interactions between the environment, logistics system and economy.